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Comprehensive profiling of N6-methyladnosine (m6A) readouts reveals novel m6A readers that regulate human embryonic stem cell differentiation [GLORI-seq]

GSE290408 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/02/09 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
12 samples: 6 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Comprehensive Profiling of N(6)-methyladnosine (m(6)A) Readouts Reveals Novel m(6)A Readers That Regulate Human Embryonic Stem Cell DifferentiationAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
Summary (written by the submitters)
GLORI-Seq is an antibody-independent sequencing, which can detect m6A sites at single-base. The core of GLORI-Seq is to find a catalytic system of glyoxal and nitrite through chemical reaction combination screening, which efficiently deaminate unmethylated adenosine A to form inosine (A-to-I, > 98%). Inosine is read to guanosine (G) in the sequencing process, forming A-to-G conversion. GLORI-Seq enables single base detection of single base m6A by detecting the proportion of “A” in the sequenced read sequence.
Published in
Comprehensive Profiling of N(6)-methyladnosine (m(6)A) Readouts Reveals Novel m(6)A Readers That Regulate Human Embryonic Stem Cell Differentiation
Huang Z, Liu R, Wubulikasimu Z et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2026 · PMID 41560347 · doi:10.1002/advs.202510075
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE290408_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1227924 and SRA study SRP566193. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in duplicate
A549, WT, ×2 hESC, WT, ×2 JURKAT, WT, ×2 A549, METTL3 KD, ×2 hESC, METTL3 KD, ×2 JURKAT, METTL3 KD, ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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